1. Bidding strategies in
deregulated power market
By
K.Gautham Reddy
2011A8PS364G

2. Deregulation
 Deregulation is the process of removing or reducing state
regulations. It is therefore opposite of regulation, which refers to the
process of the government regulating certain activities.
 Energy prices are not regulated in these deregulated areas and
consumers are not forced to receive supply from their utility.
 Deregulation allows competitive energy suppliers to enter the
markets
 Deregulation gives consumers choice - the power of the buyer. A
deregulated market allows you to choose your commodity supplier.

3. Bidding classifications
 Buying or selling of energy takes place
in the form of bids
Bidding

4. Bidding in electricity market
• Agents submit bids (Quantity and cost) to either buy or sell
energy.
• Independent System Operator (ISO) matches the bids

5. Market clearing price
• Bids below MCP are
accpeted
• Two types of payments
for bids
i) Uniform pricing
ii) Pay as bid
Strategic bidding:Aim is to construct best optimal bid knowing their own
costs, technical constraints and their expectation of rival and market
behavior

6. Mathematical formulation
 Consider total of ‘m’ suppliers
 Uniform pricing method is followed
 The jth supplier bid with linear supply curve
denoted by Gj (Pj ) = aj + bj Pj
Pj is the active power output, aj and bj are non-
negative bidding coefficients of the jth supplier.

7. 

8. 

9. Profit maximization
 Hence our main objective is to maximize profits
which is the difference between the selling price
and the production price which is as follows
 The objective is to determine bidding coefficients aj
and bj so as to maximize F(aj,bj) subject to
equations 1 and 2.

10. Gravitational search algorithm
 Follows two basic laws
i) Law of gravity: Each particle attracts every other particle and the
gravitational force between two particles is directly proportional to the product
of their masses and inversely proportional to the distance ‘R’ between them.
ii) Law of motion :The current velocity of any mass is equal to the sum of the
fraction of its previous velocity of mass and the variation in the velocity.

11. 

12. 

13.  Fuzzification:
Inputs :
(i) normalized fitness value (NFV)
(ii) current gravitational constant (G)
Outputs:
The correction of the gravitational constant (dG).

14.  Input variables represented by three linguistic values, S
(small), M (medium) and L (large) where as output variable
(G) is presented in three fuzzy sets of linguistic values; NE
(negative), ZE (zero) and PE (positive) with associated
triangular membership functions.

15. 

16. Genetic algorithm

17. References
 J. Vijaya Kumar, D.M. Vinod Kumar, K. Edukondalu,”
Strategic bidding using fuzzy adaptive gravitational search
algorithm in a pool based electricity market”, Applied Soft
Computing 13 (2013) 2445–2455
 Li Yang, Fushuan Wen , F.F. Wu , Yixini Ni and Jiaju Qiu,”
Development of Bidding Strategies in Electricity Markets
Using Possibility Theory”, International Conference on Power
System Technology Proceedings, Kunming , China , 13-17
October 2002,v.1p. 182-187.
 A. Azadeh, S.F. Ghaderi, B. Pourvalikhan Nokhandan, M.
Sheikhalishahi,” A new genetic algorithm approach for
optimizing bidding strategy viewpoint of profit maximization of
a generation company”, Expert Systems with Applications 39
(2012) 1565–1574